





"""simple docstring"""






import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
A			      =	logging.get_logger(__name__)


A			      =	['''model.decoder.embed_positions.weights''']

def      __A   (      a_    :int)		->		Optional[int]:
				if "emb" in name:
								__a			:    Union[str, Any]      							=	name.replace('''emb''' ,							'''model.decoder.embed_tokens''')
				if "transformer" in name:
								__a			:    Optional[Any]      							=	name.replace('''transformer''' ,							'''model.decoder''')
				if "cross_attention" in name:
								__a			:    Tuple      							=	name.replace('''cross_attention''' ,							'''encoder_attn''')
				if "linear1" in name:
								__a			:    Any      							=	name.replace('''linear1''' ,							'''fc1''')
				if "linear2" in name:
								__a			:    Any      							=	name.replace('''linear2''' ,							'''fc2''')
				if "norm1" in name:
								__a			:    Union[str, Any]      							=	name.replace('''norm1''' ,							'''self_attn_layer_norm''')
				if "norm_cross" in name:
								__a			:    int      							=	name.replace('''norm_cross''' ,							'''encoder_attn_layer_norm''')
				if "norm2" in name:
								__a			:    str      							=	name.replace('''norm2''' ,							'''final_layer_norm''')
				if "out_norm" in name:
								__a			:    Dict      							=	name.replace('''out_norm''' ,							'''model.decoder.layer_norm''')
				if "linears" in name:
								__a			:    List[str]      							=	name.replace('''linears''' ,							'''lm_heads''')
				if "condition_provider.conditioners.description.output_proj" in name:
								__a			:    str      							=	name.replace('''condition_provider.conditioners.description.output_proj''' ,							'''enc_to_dec_proj''')
				return name

def      __A   (      a_    :OrderedDict ,							a_    :int)		->		Tuple[Dict, Dict]:
				__a			:    Optional[int]      							=	list(state_dict.keys())
				__a			:    Any      							=	{}
				for key in keys:
								__a			:    Any      							=	state_dict.pop(a_)
								__a			:    List[Any]      							=	rename_keys(a_)
								if "in_proj_weight" in key:
												# split fused qkv proj
												__a			:    List[str]      							=	val[:hidden_size, :]
												__a			:    List[str]      							=	val[hidden_size : 2 * hidden_size, :]
												__a			:    int      							=	val[-hidden_size:, :]
								elif "enc_to_dec_proj" in key:
												__a			:    int      							=	val
								else:
												__a			:    Dict      							=	val
				return state_dict, enc_dec_proj_state_dict

def      __A   (      a_    :str)		->		MusicgenDecoderConfig:
				if checkpoint == "small":
								# default config values
								__a			:    Optional[Any]      							=	10_24
								__a			:    Tuple      							=	24
								__a			:    Union[str, Any]      							=	16
				elif checkpoint == "medium":
								__a			:    int      							=	15_36
								__a			:    List[str]      							=	48
								__a			:    List[str]      							=	24
				elif checkpoint == "large":
								__a			:    int      							=	20_48
								__a			:    Tuple      							=	48
								__a			:    Optional[Any]      							=	32
				else:
								raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}.""")
				__a			:    Tuple      							=	MusicgenDecoderConfig(
				    hidden_size=a_ ,							ffn_dim=hidden_size * 4 ,							num_hidden_layers=a_ ,							num_attention_heads=a_ ,							)
				return config

@torch.no_grad()
def      __A   (      a_    :Any ,							a_    :List[Any]=None ,							a_    :List[Any]=None ,							a_    :Optional[Any]="cpu")		->		str:
				__a			:    Dict      							=	MusicGen.get_pretrained(a_ ,							device=a_)
				__a			:    str      							=	decoder_config_from_checkpoint(a_)

				__a			:    List[str]      							=	fairseq_model.lm.state_dict()
				__a							,    __a			:    str      							=	rename_state_dict(
				    a_ ,							hidden_size=decoder_config.hidden_size)

				__a			:    Dict      							=	TaEncoderModel.from_pretrained('''t5-base''')
				__a			:    Optional[Any]      							=	EncodecModel.from_pretrained('''facebook/encodec_32khz''')
				__a			:    Any      							=	MusicgenForCausalLM(a_).eval()

				# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
				__a							,    __a			:    Optional[Any]      							=	decoder.load_state_dict(a_ ,							strict=a_)

				for key in missing_keys.copy():
								if key.startswith(('''text_encoder''', '''audio_encoder''')) or key in EXPECTED_MISSING_KEYS:
												missing_keys.remove(a_)

				if len(a_) > 0:
								raise ValueError(F"""Missing key(s) in state_dict: {missing_keys}""")

				if len(a_) > 0:
								raise ValueError(F"""Unexpected key(s) in state_dict: {unexpected_keys}""")

				# init the composite model
				__a			:    int      							=	MusicgenForConditionalGeneration(text_encoder=a_ ,							audio_encoder=a_ ,							decoder=a_)

				# load the pre-trained enc-dec projection (from the decoder state dict)
				model.enc_to_dec_proj.load_state_dict(a_)

				# check we can do a forward pass
				__a			:    Optional[int]      							=	torch.arange(0 ,							8 ,							dtype=torch.long).reshape(2 ,							-1)
				__a			:    Optional[int]      							=	input_ids.reshape(2 * 4 ,							-1)

				with torch.no_grad():
								__a			:    List[str]      							=	model(input_ids=a_ ,							decoder_input_ids=a_).logits

				if logits.shape != (8, 1, 20_48):
								raise ValueError('''Incorrect shape for logits''')

				# now construct the processor
				__a			:    Optional[int]      							=	AutoTokenizer.from_pretrained('''t5-base''')
				__a			:    Optional[int]      							=	AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz''' ,							padding_side='''left''')

				__a			:    Optional[Any]      							=	MusicgenProcessor(feature_extractor=a_ ,							tokenizer=a_)

				# set the appropriate bos/pad token ids
				__a			:    Any      							=	20_48
				__a			:    Optional[int]      							=	20_48

				# set other default generation config params
				__a			:    str      							=	int(30 * audio_encoder.config.frame_rate)
				__a			:    Tuple      							=	True
				__a			:    List[Any]      							=	3.0

				if pytorch_dump_folder is not None:
								Path(a_).mkdir(exist_ok=a_)
								logger.info(F"""Saving model {checkpoint} to {pytorch_dump_folder}""")
								model.save_pretrained(a_)
								processor.save_pretrained(a_)

				if repo_id:
								logger.info(F"""Pushing model {checkpoint} to {repo_id}""")
								model.push_to_hub(a_)
								processor.push_to_hub(a_)


if __name__ == "__main__":
		A			      =	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint''',
		    default='''small''',
		    type=str,
		    help='''Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder''',
		    required=True,
		    default=None,
		    type=str,
		    help='''Path to the output PyTorch model directory.''',
		)
		parser.add_argument(
		    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
		)
		parser.add_argument(
		    '''--device''', default='''cpu''', type=str, help='''Torch device to run the conversion, either cpu or cuda.'''
		)

		A			      =	parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)

from ..utils import is_flax_available, is_torch_available


if is_torch_available():
							from .autoencoder_kl import AutoencoderKL
							from .controlnet import ControlNetModel
							from .dual_transformer_ad import DualTransformeraDModel
							from .modeling_utils import ModelMixin
							from .prior_transformer import PriorTransformer
							from .ta_film_transformer import TaFilmDecoder
							from .transformer_ad import TransformeraDModel
							from .unet_ad import UNetaDModel
							from .unet_ad import UNetaDModel
							from .unet_ad_condition import UNetaDConditionModel
							from .unet_ad_condition import UNetaDConditionModel
							from .vq_model import VQModel

if is_flax_available():
							from .controlnet_flax import FlaxControlNetModel
							from .unet_ad_condition_flax import FlaxUNetaDConditionModel
							from .vae_flax import FlaxAutoencoderKL






